A UGV-based modular robotic manipulator for soil sampling and terramechanics investigations
Bibliographic record
Abstract
Unmanned vehicles are a natural choice for accessing challenging or hazardous terrains, for instance oil sands tailings ponds, and performing tasks such as soil sampling and terramechanics investigations. In previously published work, an unmanned ground vehicle (UGV) named RTC-I was designed and built for this task by part of our group. The present article covers the design choices and technical details of a custom-built robotic manipulator, a soil sampler, and an instrumented wheel deployed onboard a second-generation UGV named RTC-II. The robotic manipulator is designed to provide the reach, payload capacity, ruggedness, and self-locking operation required for field operations. The soil sampler employs a curved scoop to minimize deformation of the collected sample. The instrumented wheel permits independent control of the normal load and the slip ratio during terramechanics investigations. Each of the three designs is deployed and successfully tested in field experiments. Measurements collected by the soil sampler and instrumented wheel will be used in future work dealing with sampler force modeling and real-time terrain parameter estimation, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".